Associative memory is a fundamental property of the nervous system. It allows us to retrieve memories from partial or corrupted input, and thus plays a critical role in processing and categorizing information. The mechanisms that underlie associative memory, however, are not yet understood. The leading model, a theoretical model, proposes that the nervous system performs associative memory by implementing attractor networks. In such networks, input, in the form of patterns of action potentials, provides partial information about a memory; the dynamics of the network then drives the neural activity to an attractor - a stable state in activity space - that corresponds to a complete representation of the memory. While the attractor model is a valuable construct, it is an idealized one - there is a large gap between the model and real neuronal networks. Our goal is to close this gap, so that the attractor hypothesis can be rigorously tested. To do that, we will construct biologically realistic models that match the properties of specific brain areas associated with memory-related tasks, such as prefrontal, parietal and inferotemporal cortex. These models, which we will analyze using mean-field theory and large-scale simulations, will allow us to make experimentally testable predictions. Those predictions can then be used to determine whether attractor networks exist in the brain, and if so, what their underlying structure is.